Controlled Memory Interference in Continual LLM Agents

# Controlled Memory Interference in Continual LLM Agents
Source: [https://arxiv.org/html/2608.07622](https://arxiv.org/html/2608.07622)
Ao Ding1,\*,Hongzong Li2,3,\*,Shiqin Tang4,Li Zhang5, Liang Chen6,Xuyang Chen7,Zi Liang5,† 1China University of Geosciences \(Beijing\)2Northwestern Polytechnical University 3The Hong Kong University of Science and Technology4Chinese Academy of Sciences 5The Hong Kong Polytechnic University6École polytechnique fédérale de Lausanne 7National University of Singapore aoding2001@gamil\.com; lihongzong@nwpu\.edu\.cn; lihongzong@ust\.hk shiqin\.tang@cair\.cas\.org\.hk; zanly@mail\.ustc\.edu\.cn; lchen@se\.cuhk\.hk xuyang\.chen@nus\.edu\.sg; zi1415926\.liang@connect\.polyu\.hk

###### Abstract

Long\-term memory enables AI agents to maintain continuity across sessions, personalize behavior, and evolve through accumulated experience\. Yet memory evolution is not simply a process of storing more information: new experiences may reinforce, revise, or interfere with existing memory states\. Existing systems mainly emphasize memory construction and relevance\-based retrieval, but several memories may remain simultaneously relevant while differing in state, temporal validity, or authority\. We introduceControlledMemoryInterference \(CMI\), a controlled diagnostic and data\-generation framework for studying how agent memory evolves under different memory relationships\. Across controlled memory evolution, benign accumulation has limited effects, whereas relationship\-specific interference sharply suppresses update plasticity with little stability gain, either by blocking target\-memory exposure or by disrupting its downstream use\. Lexical and Dense retrieval exhibit distinct interference pathways, while poisoning is more sensitive to update\-authority cues than to recency alone\. Beyond diagnosis, CMI provides targeted examples for interference\-aware memory learning, improving the distinction between valid updates and interference\-inducing memories with no observed decrease in matched\-unnoised point estimates\. These findings show that memory evolution is shaped not only by memory scale, but also by interactions among accumulated experiences\. More broadly, memory interference emerges as an important factor for reliable continual agent memory systems\.

Controlled Memory Interference in Continual LLM Agents

Ao Ding1,\*, Hongzong Li2,3,\*, Shiqin Tang4, Li Zhang5,Liang Chen6,Xuyang Chen7,Zi Liang5,†1China University of Geosciences \(Beijing\)2Northwestern Polytechnical University3The Hong Kong University of Science and Technology4Chinese Academy of Sciences5The Hong Kong Polytechnic University6École polytechnique fédérale de Lausanne7National University of Singaporeaoding2001@gamil\.com; lihongzong@nwpu\.edu\.cn; lihongzong@ust\.hkshiqin\.tang@cair\.cas\.org\.hk; zanly@mail\.ustc\.edu\.cn; lchen@se\.cuhk\.hkxuyang\.chen@nus\.edu\.sg; zi1415926\.liang@connect\.polyu\.hk

11footnotetext:Equal contribution\.22footnotetext:Corresponding author\.## 1Introduction

Long\-term memory is becoming a fundamental capability for large language model \(LLM\)\-based agents\. Recent coding and interactive agents increasingly support persistent, tool\-using, and long\-running workflows, while memory\-augmented designs preserve experience across interactionsParket al\.\([2023](https://arxiv.org/html/2608.07622#bib.bib1)\); Packeret al\.\([2023](https://arxiv.org/html/2608.07622#bib.bib2)\); Johnstonet al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib17)\)\. As agents operate across sessions and tasks, memory enables them to preserve continuity, personalize behavior, and evolve through accumulated experience\. Memory is therefore more than an external store: it provides the experiential basis through which an agent remains consistent while continuing to adapt\.

Existing agent\-memory systems have made substantial progress in memory construction, organization, updating, and access\. Generative AgentsParket al\.\([2023](https://arxiv.org/html/2608.07622#bib.bib1)\), MemGPTPackeret al\.\([2023](https://arxiv.org/html/2608.07622#bib.bib2)\), MemoryBankZhonget al\.\([2024](https://arxiv.org/html/2608.07622#bib.bib3)\), Mem0Chhikaraet al\.\([2025](https://arxiv.org/html/2608.07622#bib.bib4)\), and A\-MEMXuet al\.\([2025](https://arxiv.org/html/2608.07622#bib.bib5)\)provide representative approaches to long\-term memory management\. Lexical, dense, and hybrid retrieval methods further improve access to relevant historical informationRobertson and Zaragoza \([2009](https://arxiv.org/html/2608.07622#bib.bib8)\); Karpukhinet al\.\([2020](https://arxiv.org/html/2608.07622#bib.bib7)\); Lewiset al\.\([2020](https://arxiv.org/html/2608.07622#bib.bib6)\); Cormacket al\.\([2009](https://arxiv.org/html/2608.07622#bib.bib9)\)\. However, these approaches primarily address whether relevant memories can be retained and retrieved, rather than whether a retrieved memory remains appropriate for the agent’s current state\.

As memory evolves, multiple experiences may remain simultaneously relevant while differing in state, temporal validity, or authority\. An earlier preference may have been superseded by a valid update, while repeated experiences may overemphasize a state that is no longer appropriate\. This problem is functionally analogous to proactive interference and cue overload in human memory, where related prior experiences obstruct newer information or make shared retrieval cues less discriminativeUnderwood \([1957](https://arxiv.org/html/2608.07622#bib.bib15)\); Watkins and Watkins \([1975](https://arxiv.org/html/2608.07622#bib.bib16)\)\. We refer to such failures in continual agents asmemory interference, without assuming that artificial and human memory share the same internal mechanisms\. Figure[1](https://arxiv.org/html/2608.07622#S1.F1)situates this challenge within the continual agent loop and summarizes how CMI converts it into diagnostic and training scenarios\. Memory interference is broader than conventional retrieval failure\. Interference\-inducing memories may prevent the target from entering the retrieved context, or the target may be retrieved but fail to guide the downstream decision\. Failures can therefore arise across the retrieval\-to\-decision pathway\. Aggregate task accuracy, recall, or ranking scores do not by themselves identify which memory relationship caused an error or whether failure occurred before or after target exposure\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x1.png)Figure 1:Overview of memory interference and CMI in the continual agent loop\. CMI controls memory load and relationships to construct paired diagnostic and training scenarios, enabling behavioral evaluation and lightweight retrieval adaptation\. In\-domain augmentation improves noisy\-update answer success across TravelPlanner and four external sources\.To study this problem, we introduceControlled Memory Interference \(CMI\), a controlled diagnostic and data\-generation framework for agent\-memory evolution\. CMI varies memory scale and relationships among accumulated experiences while preserving the query, target state, and expected decision\. We instantiate it asCMI\-Travel, a controlled extension of the Travel Planner tasks in MemoryArenaHeet al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib14)\)\. CMI\-Travel reconstructs memory trajectories with progressive load levels, relationship\-controlled interventions, paired behavioral probes, and authority\-sensitive poisoning conditions\. It evaluates valid update adoption \(*plasticity*\), preservation of established states \(*stability*\), and resistance to misleading memories \(*poisoning resistance*\)\.

Our experiments uncoverselective plasticity suppression during memory evolution\. Benign accumulation produces limited and non\-monotonic changes, whereas repeated history and same\-slot conflict substantially impair valid update adoption with little corresponding stability gain\. The phenomenon is therefore relationship\-specific and asymmetric rather than a general capacity limitation or a symmetric stability–plasticity trade\-off\. A retrieval\-to\-decision decomposition further shows that interference may arise either before or after target exposure\. Lexical Retrieval is particularly sensitive to repeated surface cues and direct same\-slot conflict, whereas Dense retrieval is vulnerable to semantic crowding among related memories\. Poisoning is similarly role\-sensitive: persistent\-update and authority cues are more influential than recency alone\.

Beyond diagnosis, CMI generates targeted examples forinterference\-aware memory learning\. Incorporating CMI\-generated data improves the distinction between valid updates and interference\-inducing memories with no observed decrease in matched\-unnoised point estimates\. Observable retrieval\-state signals also support adaptive lexical–dense access, improving target ordering and downstream memory\-state decisions\. Memory interference is thus not only measurable, but also an actionable target for improving continual memory reliability\.

Our contributions are summarized as follows:

1. i\.We identifyselective plasticity suppressionduring memory evolution, a relationship\- and architecture\-dependent phenomenon that cannot be explained by memory scale alone\.
2. ii\.We introduceCMI,a portable controlled diagnosticanddata\-generation framework, and validate it across multiple datasets\.
3. iii\.We show that in\-domain CMI augmentation improves target access, target rank, and noisy\-update answer success, with no observed decrease in matched\-unnoised point estimates\.

## 2Related Work

#### Long\-term memory and retrieval for agents\.

Agent\-memory systems support long\-running interaction through memory storage, organization, reflection, and updating\. Generative AgentsParket al\.\([2023](https://arxiv.org/html/2608.07622#bib.bib1)\), MemGPTPackeret al\.\([2023](https://arxiv.org/html/2608.07622#bib.bib2)\), MemoryBankZhonget al\.\([2024](https://arxiv.org/html/2608.07622#bib.bib3)\), Mem0Chhikaraet al\.\([2025](https://arxiv.org/html/2608.07622#bib.bib4)\), and A\-MEMXuet al\.\([2025](https://arxiv.org/html/2608.07622#bib.bib5)\)represent major approaches to persistent agent memory\. Lexical, dense, and hybrid retrieval further improve access to relevant historyRobertson and Zaragoza \([2009](https://arxiv.org/html/2608.07622#bib.bib8)\); Karpukhinet al\.\([2020](https://arxiv.org/html/2608.07622#bib.bib7)\); Lewiset al\.\([2020](https://arxiv.org/html/2608.07622#bib.bib6)\); Cormacket al\.\([2009](https://arxiv.org/html/2608.07622#bib.bib9)\)\. However, relevance\-based access does not determine which memory is appropriate when several retrieved experiences represent different states or temporal roles\. Recent benchmarks evaluate long\-horizon recall, state updates, evolving preferences, and multi\-entity dynamicsWuet al\.\([2025](https://arxiv.org/html/2608.07622#bib.bib18)\); Heet al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib14)\); Liuet al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib19)\); Liet al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib20)\); Junget al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib21)\)\. Memory\-poisoning studies further show that untrusted stored examples can steer retrieval\-augmented agentsChenet al\.\([2024](https://arxiv.org/html/2608.07622#bib.bib30)\)\.

#### Interference in continual systems\.

Continual\-learning research studies how new information disrupts previously acquired knowledge, including the stability–plasticity dilemma and methods such as EWC, GEM, and experience replayKirkpatricket al\.\([2017](https://arxiv.org/html/2608.07622#bib.bib10)\); Lopez\-Paz and Ranzato \([2017](https://arxiv.org/html/2608.07622#bib.bib12)\); Rolnicket al\.\([2019](https://arxiv.org/html/2608.07622#bib.bib11)\); Parisiet al\.\([2019](https://arxiv.org/html/2608.07622#bib.bib13)\)\. These works primarily address interference during parameter learning or replay\. We instead study interactions among externally stored memories during retrieval and downstream decision making\.

## 3Methodology

![Refer to caption](https://arxiv.org/html/2608.07622v1/x2.png)Figure 2:CMI controls memory load and relationship composition to construct paired behavioral probes and selector\-training cases\.We study memory interference in continual agents that accumulate external memories over time\. Several memories may be relevant to the same decision while differing in state, temporal validity, or authority\. CMI isolates these factors through paired controlled scenarios and uses the resulting cases for behavioral diagnosis and interference\-aware training, as summarized in Figure[2](https://arxiv.org/html/2608.07622#S3.F2)\.

### 3\.1Problem Formulation and Behavioral Dimensions

Letiiindex a source case\. Each case contains a queryqiq\_\{i\}, an expected decisionyiy\_\{i\}, and a memory collection

ℳi=\{mi,1,…,mi,Ni\},\\mathcal\{M\}\_\{i\}=\\\{m\_\{i,1\},\\ldots,m\_\{i,N\_\{i\}\}\\\},wheremi,jm\_\{i,j\}is thejj\-th memory andNi=\|ℳi\|N\_\{i\}=\|\\mathcal\{M\}\_\{i\}\|is the number of memories\. Given a retrieval depthkk, a memory\-access mechanism returns the ordered sequence

Rk​\(qi,ℳi\)=\(mi,\(1\),…,mi,\(k\)\),R\_\{k\}\(q\_\{i\},\\mathcal\{M\}\_\{i\}\)=\\bigl\(m\_\{i,\(1\)\},\\ldots,m\_\{i,\(k\)\}\\bigr\),whereRkR\_\{k\}denotes the access mechanism andmi,\(r\)m\_\{i,\(r\)\}is the memory ranked at positionrr\.

For evaluation, we partition the collection into three disjoint sets:

ℳi=ℳi\+∪˙ℳic∪˙ℳio,\\mathcal\{M\}\_\{i\}=\\mathcal\{M\}\_\{i\}^\{\+\}\\mathbin\{\\dot\{\\cup\}\}\\mathcal\{M\}\_\{i\}^\{c\}\\mathbin\{\\dot\{\\cup\}\}\\mathcal\{M\}\_\{i\}^\{o\},where∪˙\\dot\{\\cup\}denotes disjoint union\.ℳi\+\\mathcal\{M\}\_\{i\}^\{\+\}contains the*target memories*that should influence the current decision,ℳic\\mathcal\{M\}\_\{i\}^\{c\}contains*competing memories*that are relevant but inappropriate for the current state, andℳio\\mathcal\{M\}\_\{i\}^\{o\}contains other controlled or background memories, including neutral, unrelated, and task\-compatible same\-state additions\. Competing memories may encode superseded states, transient exceptions, same\-slot conflicts, or deliberately misleading updates\.

Memory interference occurs when relationships among these memories alter which state influences the final decision\. We evaluate three behavioral dimensions\.Plasticitymeasures whether the agent adopts a valid persistent update instead of following a superseded state\.Stabilitymeasures whether it preserves an established state when newer evidence is transient or context\-specific\.Poisoning resistancemeasures whether it rejects an invalid or misleading update\. These dimensions represent distinct memory roles and do not assume a symmetric stability–plasticity trade\-off\.

### 3\.2Controlled Memory Interference

CMI applies to source cases in which an old state, a distinct current state, and supporting historical evidence can be recovered\. For each eligible caseii, we construct a base memory collectionℬi\\mathcal\{B\}\_\{i\}containing the target state and the context required for the original decision\. A controlled scenario is then defined as

ℳic,ℓ=ℬi∪Δic,ℓ,\|Δic,ℓ\|=nℓ,\\mathcal\{M\}\_\{i\}^\{c,\\ell\}=\\mathcal\{B\}\_\{i\}\\cup\\Delta\_\{i\}^\{c,\\ell\},\\qquad\|\\Delta\_\{i\}^\{c,\\ell\}\|=n\_\{\\ell\},wherec∈𝒞c\\in\\mathcal\{C\}indexes a memory\-relationship condition,ℓ∈ℒ\\ell\\in\\mathcal\{L\}indexes a memory\-load level,Δic,ℓ\\Delta\_\{i\}^\{c,\\ell\}is the set of controlled additions, andnℓn\_\{\\ell\}is the number of additions at levelℓ\\ell\.𝒞\\mathcal\{C\}andℒ\\mathcal\{L\}denote the sets of evaluated relationship conditions and load levels, respectively, andℳic,ℓ\\mathcal\{M\}\_\{i\}^\{c,\\ell\}is the resulting CMI scenario\.

All variants derived from the same source case preserve the underlying task:

qic,ℓ=qi,yic,ℓ=yi,ℳi\+,c,ℓ=ℳi\+,q\_\{i\}^\{c,\\ell\}=q\_\{i\},\\qquad y\_\{i\}^\{c,\\ell\}=y\_\{i\},\\qquad\\mathcal\{M\}\_\{i\}^\{\+,c,\\ell\}=\\mathcal\{M\}\_\{i\}^\{\+\},whereqic,ℓq\_\{i\}^\{c,\\ell\},yic,ℓy\_\{i\}^\{c,\\ell\}, andℳi\+,c,ℓ\\mathcal\{M\}\_\{i\}^\{\+,c,\\ell\}denote the query, expected decision, and target\-memory set in the transformed scenario\. We additionally preserve the target position and the source state semantics\. Thus, differences among paired variants can be attributed to the controlled memory additions rather than changes in the underlying task\.

CMI uses three complementary protocols\.Memory loadvaries accumulated history from L0 to L6\. At fixed query and count,relationship compositioncompares matched neutral additions, unrelated memories, repeated history, and same\-slot conflict at counts 0, 2, 4, and 8\. A separatefixed\-L3 authority probeframes an invalid memory as ordinary noise \(P0\), a recent transient mention \(P1\), an explicit persistent update \(P2\), or an authoritative correction \(P3\)\. Thus, P0–P3 are not additional conditions in the four\-way composition sweep\. These protocols separate memory quantity, scope, repetition, conflict, recency, and conveyed update authority\. The resultingpaired CMI scenarioscan serve as diagnostic cases or as training examples\. CMI does not assume that arbitrary datasets can be transformed; the source case must provide a recoverable state transition and sufficient evidence to identify the superseded and current states\.

### 3\.3Diagnostic and Training Uses

#### Retrieval diagnostics\.

Letri​\(m\)r\_\{i\}\(m\)denote the rank assigned to memorymmfor caseii\. Ifmmis absent from the returned top\-kksequence, we setri​\(m\)=\+∞r\_\{i\}\(m\)=\+\\infty\. The best target and competing ranks are

ri\+=minm∈ℳi\+⁡ri​\(m\),ric=minm∈ℳic⁡ri​\(m\)\.r\_\{i\}^\{\+\}=\\min\_\{m\\in\\mathcal\{M\}\_\{i\}^\{\+\}\}r\_\{i\}\(m\),\\qquad r\_\{i\}^\{c\}=\\min\_\{m\\in\\mathcal\{M\}\_\{i\}^\{c\}\}r\_\{i\}\(m\)\.Let𝕀​\[⋅\]\\mathbb\{I\}\[\\cdot\]denote the indicator function\. We define

Ti\\displaystyle T\_\{i\}=𝐈​\[ri\+≤k\],\\displaystyle=\\mathbf\{I\}\[r\_\{i\}^\{\+\}\\leq k\],Ci\\displaystyle C\_\{i\}=𝐈​\[ric≤k\],\\displaystyle=\\mathbf\{I\}\[r\_\{i\}^\{c\}\\leq k\],POi\\displaystyle\\mathrm\{PO\}\_\{i\}=𝐈​\[ri\+≤k∧\(ric\>k∨ri\+<ric\)\],\\displaystyle=\\mathbf\{I\}\\\!\\left\[r\_\{i\}^\{\+\}\\leq k\\land\\left\(r\_\{i\}^\{c\}\>k\\lor r\_\{i\}^\{\+\}<r\_\{i\}^\{c\}\\right\)\\right\],MRRi\\displaystyle\\mathrm\{MRR\}\_\{i\}=\{1/ri\+,ri\+<\+∞,0,otherwise,\\displaystyle=CEi\\displaystyle\\mathrm\{CE\}\_\{i\}=Ti​Ci\.\\displaystyle=T\_\{i\}C\_\{i\}\.Here,TiT\_\{i\}indicates target exposure,CiC\_\{i\}indicates competing\-memory exposure,POi\\mathrm\{PO\}\_\{i\}indicates that the best target outranks every retrieved competitor,MRRi\\mathrm\{MRR\}\_\{i\}is the reciprocal rank of the best target, andCEi\\mathrm\{CE\}\_\{i\}indicates co\-exposure\. Dataset\-level Target Recall@kk, Preference Ordering, MRR, and Co\-exposure are obtained by averaging the corresponding per\-case quantities\. MRR follows the standard reciprocal\-rank evaluation conventionVoorhees and Tice \([2000](https://arxiv.org/html/2608.07622#bib.bib27)\); Preference Ordering and Co\-exposure are defined for this study\.

#### Retrieval\-to\-decision decomposition\.

LetAi∈\{0,1\}A\_\{i\}\\in\\\{0,1\\\}denote answer success, withAi=1A\_\{i\}=1when the agent output agrees with the expected decisionyiy\_\{i\}\. Under the diagnostic evaluator, target absence implies failure, soAi=0A\_\{i\}=0wheneverTi=0T\_\{i\}=0\. IfAAandTTdenote the Bernoulli variables obtained by uniformly sampling a case and taking its valuesAiA\_\{i\}andTiT\_\{i\}, then

Pr⁡\(A=1\)=Pr⁡\(T=1\)​Pr⁡\(A=1∣T=1\)\.\\Pr\(A=1\)=\\Pr\(T=1\)\\Pr\(A=1\\mid T=1\)\.We additionally reportPr⁡\(A=1∣T=1,C=1\)\\Pr\(A=1\\mid T=1,C=1\), whereCCis the Bernoulli variable induced byCiC\_\{i\}, to distinguish target\-access failure from downstream ambiguity after both target and competing memories enter the context\. These conditional quantities are descriptive diagnostics rather than causal effects\.

#### CMI\-guided retrieval adaptation\.

We instantiate CMI\-guided adaptation as an*Interference\-Aware Adaptive Retrieval*\(IAAR\) selector\. We consider two complementary access families:Lexical Retrieval, which uses token\-level matching signals, andDense Retrieval, which uses semantic similarity in an embedding space\. Their concrete implementations are specified in Section[4\.2](https://arxiv.org/html/2608.07622#S4.SS2)\.

For caseiiand memorymm, lets^L​\(qi,m\)\\widehat\{s\}\_\{\\mathrm\{L\}\}\(q\_\{i\},m\)ands^D​\(qi,m\)\\widehat\{s\}\_\{\\mathrm\{D\}\}\(q\_\{i\},m\)denote normalized Lexical and Dense Retrieval scores\. A lightweight selectorggmaps an observable retrieval\-state feature vector𝐱i\\mathbf\{x\}\_\{i\}to a mixture weight:

αi=g​\(𝐱i\),αi∈\{0,0\.25,0\.5,0\.75,1\}\.\\alpha\_\{i\}=g\(\\mathbf\{x\}\_\{i\}\),\\qquad\\alpha\_\{i\}\\in\\\{0,0\.25,0\.5,0\.75,1\\\}\.The combined score is

si​\(m\)=αi​s^L​\(qi,m\)\+\(1−αi\)​s^D​\(qi,m\),s\_\{i\}\(m\)=\\alpha\_\{i\}\\widehat\{s\}\_\{\\mathrm\{L\}\}\(q\_\{i\},m\)\+\(1\-\\alpha\_\{i\}\)\\widehat\{s\}\_\{\\mathrm\{D\}\}\(q\_\{i\},m\),wheresi​\(m\)s\_\{i\}\(m\)is the final retrieval score,αi=1\\alpha\_\{i\}=1gives pure Lexical Retrieval, andαi=0\\alpha\_\{i\}=0gives pure Dense Retrieval\. The feature vector𝐱i\\mathbf\{x\}\_\{i\}contains only observable retrieval\-state signals, such as score margin, retrieval entropy, lexical overlap, semantic similarity, duplicate density, and cross\-retriever agreement\. Target and competing\-memory annotations are not available to the deployed selector\.

To test whether CMI scenarios provide useful supervision, we compare clean\-only training with in\-domain CMI augmentation:

𝒟aug=𝒟clean∪𝒟CMI,\\mathcal\{D\}\_\{\\mathrm\{aug\}\}=\\mathcal\{D\}\_\{\\mathrm\{clean\}\}\\cup\\mathcal\{D\}\_\{\\mathrm\{CMI\}\},where𝒟clean\\mathcal\{D\}\_\{\\mathrm\{clean\}\}is the original selector\-training set,𝒟CMI\\mathcal\{D\}\_\{\\mathrm\{CMI\}\}contains CMI\-generated noisy same\-slot cases, and𝒟aug\\mathcal\{D\}\_\{\\mathrm\{aug\}\}is the augmented training set\. The resulting selector is evaluated on held\-out cases from the same source dataset\. This protocol tests in\-domain actionability and does not assume zero\-shot transfer of a frozen selector across datasets\.

## 4Experimental Setup

### 4\.1Datasets and Protocols

We apply CMI to state\-update cases derived from TravelPlanner in MemoryArenaHeet al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib14)\), PERMALiuet al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib19)\), LongMemEvalWuet al\.\([2025](https://arxiv.org/html/2608.07622#bib.bib18)\), HorizonBenchLiet al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib20)\), and the Tracking task from MEMEJunget al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib21)\)\. TravelPlanner is used for fine\-grained mechanism analysis, including memory\-scale growth, relationship\-controlled composition, retrieval\-to\-decision decomposition, and poisoning\. The remaining sources test whether CMI\-generated same\-slot interference cases provide useful in\-domain training supervision\.

For each transformed case, CMI preserves the query, target state, and expected decision while varying memory load or relationships among accumulated memories\. Target, competing\-memory, and relationship annotations are used only for construction, training\-label generation, and offline evaluation; retrieval and answer generation observe memory text only\.

The TravelPlanner mechanism study contains 50 base scenarios: 1,400 noise\-growth instances across seven load levels, two relationship\-controlled composition protocols of 800 instances each, 1,000 fixed\-L3 poisoning instances, and a held\-out retrieval diagnostic with 10 base scenarios and 320 observations per method\. PERMA, HorizonBench, and MEME Tracking each use 50 eligible bases; LongMemEval retains 46 of 50 candidate updates after requiring a distinct recoverable old state, confidence of at least0\.80\.8, and a supporting historical evidence span\.

PERMA, HorizonBench, and MEME Tracking use 30/10/10 train, development, and test bases, yielding 630 clean and 210 CMI training cases, 280 development cases, and 280 test cases\. LongMemEval uses 27/9/10 bases, yielding 567 clean and 189 CMI training cases, 252 development cases, and 280 test cases\. All splits are grouped bybase\_idwith zero overlap\. TravelPlanner follows the same clean\-only versus CMI\-augmented comparison on its audited held\-out split\.

### 4\.2Memory Access and Selector Training

We evaluate two primary access families\.Lexical Retrievalis instantiated with BM25Robertson and Zaragoza \([2009](https://arxiv.org/html/2608.07622#bib.bib8)\)usingk1=1\.5k\_\{1\}=1\.5andb=0\.75b=0\.75\.Dense Retrievaluses Qwen3\-Embedding\-8BZhanget al\.\([2025](https://arxiv.org/html/2608.07622#bib.bib22)\)with its native 4096\-dimensional output, L2 normalization, and cosine similarity\. We also evaluate fixed Lexical–Dense fusion withα=0\.25\\alpha=0\.25and RRFCormacket al\.\([2009](https://arxiv.org/html/2608.07622#bib.bib9)\)with rank constantκ=60\\kappa=60\. All methods retrieve the top three memories from the complete candidate collection\.

The encoder\-robustness analysis repeats the same protocol with E5\-Mistral\-7B\-InstructWanget al\.\([2024](https://arxiv.org/html/2608.07622#bib.bib23)\), NV\-Embed\-v2Leeet al\.\([2024](https://arxiv.org/html/2608.07622#bib.bib24)\), and Linq\-Embed\-MistralChoiet al\.\([2024](https://arxiv.org/html/2608.07622#bib.bib25)\), using each model’s native 4096\-dimensional representation and official query/document encoding convention\.

The lightweight selector is a class\-balanced random forestBreiman \([2001](https://arxiv.org/html/2608.07622#bib.bib26)\)with 300 trees, minimum leaf size 2, and random seed 42\. It predictsα∈\{0,0\.25,0\.5,0\.75,1\}\\alpha\\in\\\{0,0\.25,0\.5,0\.75,1\\\}, whereα=0\\alpha=0denotes pure Dense Retrieval andα=1\\alpha=1denotes pure Lexical Retrieval\. Selector labels maximize a development utility combining behavior success, target MRR, and security with weights11,0\.20\.2, and0\.10\.1, respectively; ties within0\.010\.01prefer the smaller lexical weight\.

Clean\-only and CMI\-augmented selectors use the same model, observable retrieval\-state features, development protocol, and held\-out test set\. They differ only in whether CMI noisy same\-slot cases are included during training\. Training and evaluation are performed separately within each source dataset; the protocol does not assume zero\-shot transfer of a frozen selector\.

### 4\.3Answer Evaluation and Statistics

TravelPlanner composition and mirrored\-stability experiments use DeepSeek\-v4\-pro with temperature 0, reasoning disabled, and a 512\-token output limit\. Its fixed\-L3 poisoning study follows an earlier frozen DeepSeek\-v4\-flash protocol and is interpreted only within that protocolXuet al\.\([2026](https://arxiv.org/html/2608.07622#bib.bib31)\)\.

The answer\-level augmentation study uses DeepSeek\-v4\-pro with temperature 0, reasoning disabled, and a 128\-token output limit\. TravelPlanner and the four external sources each contribute 280 held\-out cases evaluated under clean\-only and CMI\-augmented training, yielding5×280×2=2,8005\\times 280\\times 2=2\{,\}800generated answers\. We observe no API or parsing failures\.

TravelPlanner outputs are scored using an audited structured memory\-state parser, while the four external sources use a deterministic normalized value matcher\. Answer success requires the expected state to be selected without adopting a competing state; wrong\-memory adoption records the converse error\. Full evaluator rules are provided in the appendix\.

We report paired 95% percentile confidence intervals using cluster bootstrap overbase\_id, resampling all variants derived from the same base togetherEfron and Tibshirani \([1993](https://arxiv.org/html/2608.07622#bib.bib28)\); Chenget al\.\([2013](https://arxiv.org/html/2608.07622#bib.bib29)\)\. The answer\-level study uses 5,000 bootstrap replicates with random seed 42\. Source filters, complete feature definitions, prompts, preprocessing details, and protocol\-specific statistics are provided in the appendix\.

## 5Results and Analysis

We organize the analysis around four questions: whether memory scale alone explains degradation, which memory relationships determine the failure regime, where interference arises along the retrieval\-to\-decision pathway, and whether CMI\-generated cases provide useful training supervision\. Behavioral and retrieval protocols are reported separately because they evaluate different objectives\.

### 5\.1Memory Scale and Relationship\-Specific Interference

We first separate memory quantity from memory relationships\. Under TravelPlanner noise\-only growth, average plasticity changes from 0\.510 at L0 to 0\.455 at L6, while stability changes from 0\.200 to 0\.290\. The trajectories are non\-monotonic: plasticity reaches its lowest aggregate value around L4 and partially recovers at later loads\. Thus, increasing memory quantity affects access but does not produce a consistent collapse\. Full L0–L6 curves are provided in the appendix\.

We next hold memory count fixed while varying how added memories relate to the current decision state\. The TravelPlanner composition protocol compares matched neutral, unrelated, repeated\-history, and same\-slot conflict conditions at addition counts0,22,44, and88\. All variants derived from the same base case share the query, target state, target position, expected decision, and count\-0 baseline\.

Figure[3](https://arxiv.org/html/2608.07622#S5.F3)reports plasticity and mirrored stability across the four conditions\. Table[1](https://arxiv.org/html/2608.07622#S5.T1)summarizes the count\-8 outcomes relative to the shared count\-0 baselines \(P0=0\.84P\_\{0\}=0\.84andS0=0\.92S\_\{0\}=0\.92\)\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x3.png)Figure 3:Relationship\-controlled interference on TravelPlanner\.Table 1:TravelPlanner count\-8 behavior relative to shared count\-0 baselines \(P0=0\.84P\_\{0\}=0\.84,S0=0\.92S\_\{0\}=0\.92\)\.Relationship, rather than count, determines the failure regime\. Lexical plasticity changes by only−0\.02\-0\.02under unrelated additions, but by−0\.34\-0\.34under repeated history and−0\.50\-0\.50under same\-slot conflict\. At count 8, the paired contrasts against unrelated additions are−0\.32\-0\.32\(95% CI\[−0\.46,−0\.20\]\[\-0\.46,\-0\.20\]\) and−0\.48\-0\.48\(\[−0\.62,−0\.34\]\[\-0\.62,\-0\.34\]\)\. Dense Retrieval is more sensitive to semantic crowding: unrelated additions reduce plasticity by−0\.18\-0\.18, while repeated history and same\-slot conflict reduce it by−0\.46\-0\.46and−0\.44\-0\.44\.

The stability response is not a symmetric inverse of plasticity\. Repeated history leaves stability unchanged for both access families, whereas Dense same\-slot conflict reduces stability from 0\.92 to 0\.54\. Relationship\-specific interference therefore ranges from selective update suppression to joint degradation, rather than a universal stability–plasticity trade\-off\.

Figure[4](https://arxiv.org/html/2608.07622#S5.F4)shows that this relationship effect is robust to the Dense encoder\. Repeating the fixed TravelPlanner retrieval protocol with Qwen3\-Embedding\-8B, E5\-Mistral\-7B, NV\-Embed\-v2, and Linq\-Embed\-Mistral, all using native unprojected 4096\-dimensional representations, yields count\-8 Target Recall@3 losses of 0\.22–0\.68 under repeated history and 0\.42–0\.58 under same\-slot conflict\. Thus, the qualitative interference pattern is not specific to Qwen, although its magnitude remains encoder dependent\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x4.png)Figure 4:Encoder robustness under CMI\. Target Recall@3 is shown as relationship\-controlled memories are added\. Error bars are paired base\-cluster bootstrap 95% CIs\.
### 5\.2Retrieval\-to\-Decision Pathways and Update Authority

Table[2](https://arxiv.org/html/2608.07622#S5.T2)separates target\-access failure from failure after exposure in the count\-8 TravelPlanner results\.

Table 2:Count\-8 retrieval\-to\-decision decomposition on TravelPlanner\.Lexical same\-slot conflict is primarily a retrieval bottleneck: Target Recall@3 falls to 0\.34 and competing\-only exposure rises to 0\.66, butP​\(A∣T\)=1\.00P\(A\\mid T\)=1\.00\. Repeated history combines retrieval loss \(Recall@3 of 0\.66\) with lower conditional adoption \(0\.758\)\. Dense Retrieval fails at both stages: repeated history lowers recall to 0\.48, while same\-slot conflict yields recall of 0\.54 and conditional adoption of 0\.741\. Additional co\-exposure diagnostics in the appendix show that competitor presence alone is not a monotonic failure indicator\. These conditional quantities are descriptive rather than causal\.

#### Update authority\.

At fixed L3, recency alone has negligible effect: poison acceptance under P0/P1 is\.02/\.02\.02/\.02for Lexical Retrieval and\.00/\.00\.00/\.00for Dense Retrieval\. Framing the invalid memory as a persistent update raises acceptance to\.23/\.12\.23/\.12, and authoritative correction wording further raises it to\.26/\.14\.26/\.14\. Thus, conveyed update role, rather than recency alone, drives poisoning\. Full protocol details are reported in the appendix\.

### 5\.3Cross\-Dataset Convergence

The external L0–L6 protocols progressively accumulate same\-slot historical states\. Table[3](https://arxiv.org/html/2608.07622#S5.T3)reports paired displacement on PERMA, LongMemEval, HorizonBench, and MEME Tracking\.

Table 3:L0–L6 displacement under same\-slot historical growth\.Lexical plasticity decreases by 0\.720–0\.940 while stability changes by at most\+0\.022\+0\.022; all four plasticity intervals exclude zero\. Dense plasticity losses are smaller \(0\.040–0\.120\), with stability nearly unchanged\. Complete confidence intervals and noisy\-update results are provided in the appendix\. These sources therefore show convergent, not identical, selective update suppression under accumulated superseded history\.

### 5\.4CMI\-Guided Training Improves Retrieval and Final Answers

We compare clean\-only and CMI\-augmented selectors using identical held\-out cases, retrieval budgets, features, and model configurations\. Table[4](https://arxiv.org/html/2608.07622#S5.T4)reports noisy\-update retrieval and answer results\.

Table 4:Held\-out noisy\-update performance under clean\-only and in\-domain CMI\-augmented selector training\.CMI augmentation increases Target Recall@3 by 0\.114–0\.614 and MRR by 0\.041–0\.426 across all five sources; all paired intervals for these metrics exclude zero\. Preference Ordering improves less consistently, and competing\-memory recall remains high, so the selector improves target inclusion and rank rather than fully reconciling competing states\.

Noisy\-update answer success increases by 0\.114–0\.614, with every paired interval excluding zero\. Available clean, valid\-update, and stable\-retention point estimates do not decrease\. We do not claim condition\-wise non\-inferiority because the LongMemEval stable\-retention paired interval is unavailable\. The result supports in\-domain actionability, not complete reconciliation or zero\-shot cross\-domain transfer\.

Figure[5](https://arxiv.org/html/2608.07622#S5.F5)further bounds IAAR’s scope through a backend\-calibration audit\. Relative to fixed fusion, the frozen Qwen\-trained selector changes Preference Ordering by\+0\.144\+0\.144on Qwen but by−0\.009\-0\.009,−0\.003\-0\.003, and−0\.053\-0\.053on E5, NV, and Linq, respectively\. Retraining the same selector and feature schema per backend yields changes of\+0\.150\+0\.150,\+0\.022\+0\.022, and\+0\.003\+0\.003on E5, NV, and Linq\. IAAR therefore exposes a calibration opportunity rather than a universally transferable retrieval policy\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x5.png)Figure 5:IAAR backend calibration\. Preference Ordering compares fixed fusion, RRF, the frozen Qwen\-trained selector, and a selector retrained on each backend\. Error bars are base\-cluster bootstrap 95% CIs\.

## 6Conclusion

We introduced Controlled Memory Interference \(CMI\) to study how relationships among accumulated memories affect continual agents\. TravelPlanner experiments separate memory quantity from repeated history, same\-slot conflict, and update authority, while retrieval\-to\-decision analysis shows that interference can arise before or after target exposure\. Historical\-state growth across PERMA, LongMemEval, HorizonBench, and MEME further reveals architecture\-dependent plasticity suppression\. Finally, in\-domain CMI augmentation consistently improves target access, target rank, and noisy\-update answer success across all five settings without an observed decrease on matched unnoised point estimates\. Preference ordering and competitor exclusion remain open challenges for stronger memory\-state reconciliation\. The interference pattern persists across multiple native\-4096 embedding backends, while adaptive retrieval remains calibration\-dependent rather than universally transferable\.

## References

- Random forests\.Machine Learning45\(1\),pp\. 5–32\.External Links:[Document](https://dx.doi.org/10.1023/A%3A1010933404324)Cited by:[§4\.2](https://arxiv.org/html/2608.07622#S4.SS2.p3.7)\.
- Z\. Chen, Z\. Xiang, C\. Xiao, D\. Song, and B\. Li \(2024\)AgentPoison: red\-teaming LLM agents via poisoning memory or knowledge bases\.InAdvances in Neural Information Processing Systems,Vol\.37\.Cited by:[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1)\.
- G\. Cheng, Z\. Yu, and J\. Z\. Huang \(2013\)The cluster bootstrap consistency in generalized estimating equations\.Journal of Multivariate Analysis115,pp\. 33–47\.External Links:[Document](https://dx.doi.org/10.1016/j.jmva.2012.09.003)Cited by:[§4\.3](https://arxiv.org/html/2608.07622#S4.SS3.p4.1)\.
- P\. Chhikara, D\. Khant, S\. Aryan, T\. Singh, and D\. Yadav \(2025\)Mem0: building production\-ready ai agents with scalable long\-term memory\.arXiv preprint arXiv:2504\.19413\.Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p2.1),[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1)\.
- C\. Choi, J\. Kim, S\. Lee, J\. Kwon, S\. Gu, Y\. Kim, M\. Cho, and J\. Sohn \(2024\)Linq\-Embed\-Mistral technical report\.arXiv preprint arXiv:2412\.03223\.Cited by:[§4\.2](https://arxiv.org/html/2608.07622#S4.SS2.p2.1)\.
- G\. V\. Cormack, C\. L\. Clarke, and S\. Buettcher \(2009\)Reciprocal rank fusion outperforms condorcet and individual rank learning methods\.InProceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval,pp\. 758–759\.Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p2.1),[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1),[§4\.2](https://arxiv.org/html/2608.07622#S4.SS2.p1.4)\.
- B\. Efron and R\. J\. Tibshirani \(1993\)An introduction to the bootstrap\.Chapman and Hall/CRC\.Cited by:[§4\.3](https://arxiv.org/html/2608.07622#S4.SS3.p4.1)\.
- Z\. He, Y\. Wang, C\. Zhi, Y\. Hu, T\. Chen, L\. Yin, Z\. Chen, T\. A\. Wu, S\. Ouyang, Z\. Wang,et al\.\(2026\)MemoryArena: benchmarking agent memory in interdependent multi\-session agentic tasks\.arXiv preprint arXiv:2602\.16313\.Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p4.1),[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1),[§4\.1](https://arxiv.org/html/2608.07622#S4.SS1.p1.1)\.
- D\. Johnston, D\. Holtz, A\. M\. Richmond, C\. Ong, P\. Tambe, and A\. Chatterji \(2026\)The shift to agentic ai: evidence from codex\.arXiv preprint arXiv:2606\.26959\.Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p1.1)\.
- S\. Jung, A\. Rubinstein, A\. Uselis, S\. Yun, and S\. J\. Oh \(2026\)MEME: multi\-entity and evolving memory evaluation\.arXiv preprint arXiv:2605\.12477\.Cited by:[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1),[§4\.1](https://arxiv.org/html/2608.07622#S4.SS1.p1.1)\.
- V\. Karpukhin, B\. Oguz, S\. Min, P\. Lewis, L\. Wu, S\. Edunov, D\. Chen, and W\. Yih \(2020\)Dense passage retrieval for open\-domain question answering\.InProceedings of the 2020 conference on empirical methods in natural language processing \(EMNLP\),pp\. 6769–6781\.Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p2.1),[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1)\.
- J\. Kirkpatrick, R\. Pascanu, N\. Rabinowitz, J\. Veness, G\. Desjardins, A\. A\. Rusu, K\. Milan, J\. Quan, T\. Ramalho, A\. Grabska\-Barwinska,et al\.\(2017\)Overcoming catastrophic forgetting in neural networks\.Proceedings of the national academy of sciences114\(13\),pp\. 3521–3526\.Cited by:[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px2.p1.1)\.
- C\. Lee, R\. Roy, M\. Xu, J\. Raiman, M\. Shoeybi, B\. Catanzaro, and W\. Ping \(2024\)NV\-Embed: improved techniques for training LLMs as generalist embedding models\.arXiv preprint arXiv:2405\.17428\.Cited by:[§4\.2](https://arxiv.org/html/2608.07622#S4.SS2.p2.1)\.
- P\. Lewis, E\. Perez, A\. Piktus, F\. Petroni, V\. Karpukhin, N\. Goyal, H\. Küttler, M\. Lewis, W\. Yih, T\. Rocktäschel,et al\.\(2020\)Retrieval\-augmented generation for knowledge\-intensive nlp tasks\.Advances in neural information processing systems33,pp\. 9459–9474\.Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p2.1),[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1)\.
- S\. S\. Li, B\. Paranjape, K\. Oktar, Z\. Ma, G\. Zhou, L\. Guan, N\. Zhang, S\. Park, L\. Chen, D\. Yang, Y\. Tsvetkov, and A\. Celikyilmaz \(2026\)HorizonBench: long\-horizon personalization with evolving preferences\.arXiv preprint arXiv:2604\.17283\.Cited by:[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1),[§4\.1](https://arxiv.org/html/2608.07622#S4.SS1.p1.1)\.
- S\. Liu, J\. Zhu, L\. Shu, J\. Lin, Y\. Chen, H\. Zhang, C\. Zhang, D\. Xu, J\. Li, B\. Tang, Z\. Li, F\. Xiong, E\. Chen, and T\. Xu \(2026\)PERMA: benchmarking personalized memory agents via event\-driven preference and realistic task environments\.arXiv preprint arXiv:2603\.23231\.Cited by:[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1),[§4\.1](https://arxiv.org/html/2608.07622#S4.SS1.p1.1)\.
- D\. Lopez\-Paz and M\. Ranzato \(2017\)Gradient episodic memory for continual learning\.Advances in neural information processing systems30\.Cited by:[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px2.p1.1)\.
- C\. Packer, S\. Wooders, K\. Lin, V\. Fang, S\. G\. Patil, I\. Stoica, and J\. E\. Gonzalez \(2023\)MemGPT: towards LLMs as operating systems\.arXiv preprint arXiv:2310\.08560\.Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p1.1),[§1](https://arxiv.org/html/2608.07622#S1.p2.1),[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1)\.
- G\. I\. Parisi, R\. Kemker, J\. L\. Part, C\. Kanan, and S\. Wermter \(2019\)Continual lifelong learning with neural networks: a review\.Neural networks113,pp\. 54–71\.Cited by:[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px2.p1.1)\.
- J\. S\. Park, J\. O’Brien, C\. J\. Cai, M\. R\. Morris, P\. Liang, and M\. S\. Bernstein \(2023\)Generative agents: interactive simulacra of human behavior\.InProceedings of the 36th annual acm symposium on user interface software and technology,pp\. 1–22\.Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p1.1),[§1](https://arxiv.org/html/2608.07622#S1.p2.1),[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1)\.
- S\. Robertson and H\. Zaragoza \(2009\)The probabilistic relevance framework: BM25 and beyond\.Foundations and Trends in Information Retrieval3\(4\),pp\. 333–389\.External Links:[Document](https://dx.doi.org/10.1561/1500000019)Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p2.1),[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1),[§4\.2](https://arxiv.org/html/2608.07622#S4.SS2.p1.4)\.
- D\. Rolnick, A\. Ahuja, J\. Schwarz, T\. Lillicrap, and G\. Wayne \(2019\)Experience replay for continual learning\.Advances in neural information processing systems32\.Cited by:[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px2.p1.1)\.
- B\. J\. Underwood \(1957\)Interference and forgetting\.\.Psychological review64\(1\),pp\. 49\.Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p3.1)\.
- E\. M\. Voorhees and D\. M\. Tice \(2000\)The TREC\-8 question answering track evaluation\.InText Retrieval Conference,Cited by:[§3\.3](https://arxiv.org/html/2608.07622#S3.SS3.SSS0.Px1.p1.13)\.
- L\. Wang, N\. Yang, X\. Huang, L\. Yang, R\. Majumder, and F\. Wei \(2024\)Improving text embeddings with large language models\.arXiv preprint arXiv:2401\.00368\.Cited by:[§4\.2](https://arxiv.org/html/2608.07622#S4.SS2.p2.1)\.
- M\. J\. Watkins and O\. C\. Watkins \(1975\)Buildup of proactive inhibition as a cue\-overload effect\.Journal of Experimental Psychology: Human Learning and Memory1\(4\),pp\. 442–452\.External Links:[Document](https://dx.doi.org/10.1037/0278-7393.1.4.442)Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p3.1)\.
- D\. Wu, H\. Wang, W\. Yu, Y\. Zhang, K\. Chang, and D\. Yu \(2025\)LongMemEval: benchmarking chat assistants on long\-term interactive memory\.InInternational Conference on Learning Representations,Cited by:[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1),[§4\.1](https://arxiv.org/html/2608.07622#S4.SS1.p1.1)\.
- A\. Xu, B\. Lin, B\. Xue, B\. Wang, B\. Xu, B\. Wu, B\. Zhang, C\. Lin, C\. Dong, C\. Ling,et al\.\(2026\)Deepseek\-v4: towards highly efficient million\-token context intelligence\.arXiv preprint arXiv:2606\.19348\.Cited by:[§4\.3](https://arxiv.org/html/2608.07622#S4.SS3.p1.1)\.
- W\. Xu, Z\. Liang, K\. Mei, H\. Gao, J\. Tan, and Y\. Zhang \(2025\)A\-MEM: agentic memory for LLM agents\.Advances in Neural Information Processing Systems38,pp\. 17577–17604\.Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p2.1),[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1)\.
- Y\. Zhang, M\. Li, D\. Long, X\. Zhang, H\. Lin, B\. Yang, P\. Xie, A\. Yang, D\. Liu, J\. Lin, F\. Huang, and J\. Zhou \(2025\)Qwen3 Embedding: advancing text embedding and reranking through foundation models\.arXiv preprint arXiv:2506\.05176\.Cited by:[§4\.2](https://arxiv.org/html/2608.07622#S4.SS2.p1.4)\.
- W\. Zhong, L\. Guo, Q\. Gao, H\. Ye, and Y\. Wang \(2024\)MemoryBank: enhancing large language models with long\-term memory\.Proceedings of the AAAI Conference on Artificial Intelligence38\(17\),pp\. 19724–19731\.External Links:[Document](https://dx.doi.org/10.1609/aaai.v38i17.29946)Cited by:[§1](https://arxiv.org/html/2608.07622#S1.p2.1),[§2](https://arxiv.org/html/2608.07622#S2.SS0.SSS0.Px1.p1.1)\.

## Appendix

## Appendix AScope and Reproducibility Map

The retained experimental scope comprises: \(i\) TravelPlanner\-derived L0–L6 growth, relationship\-controlled composition, mirrored stability, and fixed\-L3 update\-authority probes; \(ii\) CMI transformations of PERMA, LongMemEval, HorizonBench, and the Tracking task from MEME; \(iii\) lexical, dense, fixed fusion, and RRF access; \(iv\) clean\-only versus CMI\-augmented IAAR training; and \(v\) native\-4096 encoder robustness\. Later resolver, versioned\-memory, representation\-resolution, and router\-optimization branches are excluded\.

Table[A\.1](https://arxiv.org/html/2608.07622#A1.T1)maps the computational portions of the AAAI reproducibility checklist to this appendix and the accompanying artifact package\. The paper makes no formal theoretical contribution, so the conditional theory\-only checklist items are intentionally left unanswered\.

Table A\.1:Reproducibility\-checklist map\. Paths are relative to the uploaded reviewer\-artifact root unless stated otherwise\.
## Appendix BDatasets and Memory Construction

CMI converts eligible state\-update cases into paired continual\-memory trajectories\. These are controlled transformations of source data rather than evaluations under the sources’ native leaderboard protocols\. The transformation keeps the base query, target state, and expected decision fixed while changing memory load or relationships among memories\. Gold target and competing\-memory annotations are used only for construction, selector\-label generation, and offline evaluation; retrieval and generation observe memory text only\.

### B\.1Source Coverage and Eligibility

Table[B\.1](https://arxiv.org/html/2608.07622#A2.T1)gives the audited coverage\. TravelPlanner\-derived cases come from MemoryArena\. The external transformations use PERMA, LongMemEval, HorizonBench, and the Tracking task from MEME\. LongMemEval retained 46 of 50 candidate knowledge updates after requiring a recoverable old value distinct from the current value, confidence at least 0\.8, and a verbatim supporting span in the historical session\. The other external sources retained 50 eligible bases\.

Table B\.1:Audited CMI data coverage\. “Growth rows” count all four event types over L0–L6\. Travel composition contains separate plasticity and mirrored stability datasets\.For PERMA, HorizonBench, and MEME, the 30/10/10 base split yields 630 clean and 210 CMI training cases, 280 development cases, and 280 test cases\. LongMemEval’s 27/9/10 split yields 567 clean and 189 CMI training cases, 252 development cases, and 280 test cases\. All variants with the samebase\_idremain in one split; the supplied audits report zero base\-ID overlap\.

Table[B\.1](https://arxiv.org/html/2608.07622#A2.T1)distinguishes construction coverage from answer\-level evaluation coverage\. All four external fixed\-L3 datasets passed the structural protocol audit and now have matched BM25/Dense answer\-level poisoning summaries under the same frozen generation and corrected\-scoring protocol\.

### B\.2Transformation Audit and Representative States

Every external source passed the same structural checks: the seven load levels contain the expected number of memories; the four stress types remain paired by base; target and forbidden states differ; memory identifiers are unique; and no query contains a role annotation\. Table[B\.2](https://arxiv.org/html/2608.07622#A2.T2)summarizes the machine\-readable audit records\. The L0 row contains the event’s minimum required memories \(2–4\); L1–L6 contain 6, 12, 24, 48, 90, and 135\.

Table B\.2:External CMI protocol\-audit results\. Pairing failures are counted at the base–stress group level\.Table[B\.3](https://arxiv.org/html/2608.07622#A2.T3)illustrates the source\-dependent content replacement\. These examples are not evaluation outputs: they show how the same CMI state\-transition schema is instantiated in different domains\.

Table B\.3:Representative audited old/current state pairs used by the external CMI transformations\.
### B\.3Record Structure and Retrieval View

Each JSONL row stores a query, expected structured or normalized state, session list, memory text, timestamp, session identifier, insertion order, memory\-role metadata, target IDs, and competing or forbidden IDs\. Timestamps, sessions, and role labels support construction and offline audits\. Baseline retrieval indexes only the*memory text*; IAAR additionally uses observable rank, score, text\-overlap, embedding, count, and insertion\-order statistics defined in Section[F](https://arxiv.org/html/2608.07622#A6)\. No memory\-ID suffix, event label, target ID, or forbidden ID is provided to a deployed selector or generator\.

Construction audits check duplicate memory IDs, query\-to\-memory answer leakage, cross\-load pairing, expected/forbidden identity, memory count, and role\-label leakage\. External synthetic histories use SHA\-256\-derived deterministic content selection, making the realized datasets independent of Python hash ordering\.

## Appendix CControlled Interference Protocols

### C\.1L0–L6 Memory Growth

The nominal load profile is L0–L6 with 3, 6, 12, 24, 48, 90, and 135 memories for the Travel growth summary\. External event rows use the minimum number of memories needed by the event at L0 \(2–4\), then the same L1–L6 targets of 6, 12, 24, 48, 90, and 135\. Each base appears at every load\. The four event types are clean, valid update, stable retention, and valid update with noise\. Added history is held within the transformation protocol rather than regenerated independently for each access mechanism\.

### C\.2Relationship\-Controlled Composition

The paired composition study fixes query, target memory, gold state, target position, and base scenario\. It appends 0, 2, 4, or 8 memories under one of the four relationships in Table[C\.1](https://arxiv.org/html/2608.07622#A3.T1)\. Count zero is a shared baseline; it is generated once per base and reused across the four composition trajectories\.

Table C\.1:Paper terminology and construction of the four controlled memory relationships\.Plasticity asks whether a persistent valid update is adopted\. The mirrored stability protocol retains the same 50 bases and compositions but introduces a one\-occasion transient value and asks for the persistent/default state\. Its target is the stable value, not the transient exception\. Retrieval is rerun for the stability query; plasticity retrieval IDs are not reused\.

### C\.3Fixed\-L3 Update Authority

The fixed\-L3 protocol isolates the authority conveyed by a misleading memory\. P0 supplies ordinary unrelated/control context, P1 gives the misleading value recency without update language, P2 frames it as a persistent update, and P3 uses authoritative correction wording\. Each condition contains 100 paired cases for each reported access mechanism\. This protocol is held separate from L0–L6 growth and from relationship counts\.

## Appendix DMetrics and Evaluators

### D\.1Retrieval Metrics

LetGiG\_\{i\}be target memory IDs,FiF\_\{i\}competing or forbidden IDs, andRikR\_\{i\}^\{k\}the top\-kkranking for sampleii, withk=3k=3\. We report

Ti\\displaystyle T\_\{i\}=𝟙​\[Gi∩Rik≠∅\],\\displaystyle=\\mathbb\{1\}\[G\_\{i\}\\cap R\_\{i\}^\{k\}\\neq\\emptyset\],\(D\.1\)Ci\\displaystyle C\_\{i\}=𝟙​\[Fi∩Rik≠∅\],\\displaystyle=\\mathbb\{1\}\[F\_\{i\}\\cap R\_\{i\}^\{k\}\\neq\\emptyset\],\(D\.2\)MRRi\\displaystyle\\mathrm\{MRR\}\_\{i\}=\{1/riG,riG<∞0,otherwise,\\displaystyle=\\begin\{cases\}1/r\_\{i\}^\{G\},&r\_\{i\}^\{G\}<\\infty\\\\ 0,&\\text\{otherwise,\}\\end\{cases\}\(D\.3\)CEi\\displaystyle\\mathrm\{CE\}\_\{i\}=Ti​Ci,\\displaystyle=T\_\{i\}C\_\{i\},\(D\.4\)whereriGr\_\{i\}^\{G\}is the best target rank\. For selector evaluations, preference ordering is

POi=𝟙​\[Ti=1∧\(Ci=0∨riG<riF\)\]\.\\mathrm\{PO\}\_\{i\}=\\mathbb\{1\}\[T\_\{i\}=1\\ \\wedge\\ \(C\_\{i\}=0\\ \\vee\\ r\_\{i\}^\{G\}<r\_\{i\}^\{F\}\)\]\.\(D\.5\)Target\-only and competing\-only rates areTi​\(1−Ci\)T\_\{i\}\(1\-C\_\{i\}\)andCi​\(1−Ti\)C\_\{i\}\(1\-T\_\{i\}\)\. The score margin is the target score minus the strongest forbidden score when both exist\. Encoder\-only robustness records the complete ranking before truncation and uses target\-versus\-forbidden rank ordering; its CSV retains this protocol\-specific field separately\.

### D\.2Behavior Metrics

Plasticity is answer success on a valid persistent update\. Stability is answer success on the mirrored persistent\-state query after a transient event\. Noisy plasticity is valid\-update success with competing/noise memories present\. Poison acceptance is explicit adoption of the invalid state in the P0–P3 protocol; poisoning resistance is its complement only within that protocol\. Clean cases are not counted as stability\.

For pathway diagnostics,AiA\_\{i\}denotes answer success and is analyzed asP​\(A∣T\)P\(A\\mid T\)andP​\(A∣T,C\)P\(A\\mid T,C\)\. These conditional quantities localize failure before versus after target exposure; they are descriptive diagnostics, not causal effects\.

### D\.3Generation Prompts and Parsing

Travel composition uses the following invariant instruction, followed by the slot type, selected memory context, query, and a slot\-specific JSON schema:

> You are a strict memory\-state resolver\. Use only the stored memories below to answer the query\. If memories conflict, use the latest applicable stable update for the same traveler/day/slot\. Do not explain\. Do not copy long memory text\. Return one valid compact JSON object only\.

The mirrored stability prompt replaces the conflict sentence with: “The query asks for the persistent/default state\. If a memory is temporary, one\-time, or for a single occasion, do not treat it as the default state\.” Food outputs usecuisineandselected\_option; budget outputs usebudgetandselected\_option; accommodation outputs useroom\_type,joins\_traveler,rating\_preference,price\_preference, and a summary of at most 12 words\. JSON extraction first parses the full response, then the first enclosed JSON object\. A missing or invalid object is a parse failure, never a silent success\.

The five\-source answer\-augmentation prompt is:

> Answer the query using only the supplied memory context\. Resolve persistent updates and one\-off exceptions carefully\. Return JSON only: \{"answer": "concise answer"\}\. Do not explain your reasoning and do not mention memory IDs\.

External values are case\-folded, reduced to alphanumeric tokens, and compared after whitespace normalization\. A predicted value matches if it equals the gold, contains the gold, or is a nonempty normalized substring of the gold with length at least two\. For list\-valued LongMemEval states, every current item must match and any superseded item is forbidden\. Answer success requires a gold match and no forbidden match\. Incorrect\-memory use requires explicit adoption of a superseded value; it is not inferred as one minus answer success\.

Travel uses a slot\-aware structured parser and target\-grounded scoring for the composition mechanism analysis\. The five\-source augmentation study evaluates the predicted state directly and does not require exposure of a particular memory ID\. This distinction is preserved in all result files\.

## Appendix EMemory Access Mechanisms

Table E\.1:Final access settings used by the retained experiments\.The Qwen encoder maximum sequence length is 2048 in the external runner\. Equal retrieval scores are resolved deterministically by original insertion order\. Fixed fusion applies independent per\-query min–max normalization over the complete candidate collection:

s~r​\(m\)=sr​\(m\)−minj⁡sr​\(j\)maxj⁡sr​\(j\)−minj⁡sr​\(j\)\.\\tilde\{s\}\_\{r\}\(m\)=\\frac\{s\_\{r\}\(m\)\-\\min\_\{j\}s\_\{r\}\(j\)\}\{\\max\_\{j\}s\_\{r\}\(j\)\-\\min\_\{j\}s\_\{r\}\(j\)\}\.\(E\.1\)If the denominator is at most10−1210^\{\-12\}, every normalized score for that retriever is set to zero\. The development\-only z\-score candidate first computes\(s−μ\)/σ\(s\-\\mu\)/\\sigmaand then min–max normalizes; a standard deviation or final range at most10−1210^\{\-12\}also produces all zeros\.

The IAAR lexical implementation uses positive IDFlog⁡\(1\+\(N−d​f\+0\.5\)/\(d​f\+0\.5\)\)\\log\(1\+\(N\-df\+0\.5\)/\(df\+0\.5\)\)\. This differs from therank\-bm25baseline’s negative\-IDF behavior and is therefore recorded as a separate implementation detail rather than assumed byte\-identical\.

## Appendix FIAAR Selector

IAAR is an exploratory selector, not a new memory architecture\. It predictsα∈\{0,0\.25,0\.5,0\.75,1\}\\alpha\\in\\\{0,0\.25,0\.5,0\.75,1\\\}from inference\-time observable retrieval signals and applies the same normalized score fusion above\. The deployed feature schema contains the 18 features in Table[F\.1](https://arxiv.org/html/2608.07622#A6.T1)\.

Table F\.1:Frozen deployable IAAR feature schema\. All candidate\-set features use the union of lexical and dense top\-3 results unless stated otherwise\.For retrieval entropy, letuj=\.5​s~B,j\+\.5​s~D,ju\_\{j\}=\.5\\tilde\{s\}\_\{B,j\}\+\.5\\tilde\{s\}\_\{D,j\}across candidate IDs\. The implementation shifts by the minimum,aj=uj−minℓ⁡uℓa\_\{j\}=u\_\{j\}\-\\min\_\{\\ell\}u\_\{\\ell\}, and defines

pj=aj∑ℓaℓ,H=−∑j:pj\>0pj​ln⁡pj\.p\_\{j\}=\\frac\{a\_\{j\}\}\{\\sum\_\{\\ell\}a\_\{\\ell\}\},\\qquad H=\-\\sum\_\{j:p\_\{j\}\>0\}p\_\{j\}\\ln p\_\{j\}\.\(F\.1\)Natural logarithms are used\. If any shifted value is non\-finite or∑jaj≤10−12\\sum\_\{j\}a\_\{j\}\\leq 10^\{\-12\}, entropy is exactly zero\.

The slot heuristic applies regular expressions for food, budget, accommodation, transport, and activity\. If no semantic slot matches, it uses the first two normalized lexical tokens\. The temporal signal counts four pattern groups: current/update language, old\-state language, temporary/one\-off language, and negation; its value is capped at one\.

Inference forbidsevent\_type,stress\_type, composition labels or counts, gold or forbidden IDs, target IDs, behavior outcomes, oracle alpha, template IDs, and role\-derived memory\-ID features\. The generator never sees selector confidence, method names, retrieval scores, or these labels\.

Oracle training labels maximize

Ui​\(α\)=\\displaystyle U\_\{i\}\(\\alpha\)=\{\}Bi​\(α\)\+0\.2​MRRi​\(α\)\\displaystyle B\_\{i\}\(\\alpha\)\+2\\,\\mathrm\{MRR\}\_\{i\}\(\\alpha\)\(F\.2\)\+0\.1​Securityi​\(α\),\\displaystyle\+1\\,\\mathrm\{Security\}\_\{i\}\(\\alpha\),whereBiB\_\{i\}is strict target\-selected/forbidden\-excluded retrieval success\. Utilities within 0\.01 prefer the smaller lexical weight\. The final selector is a class\-balanced random forest with 300 trees, minimum leaf size 2, random state 42, and unrestricted parallel fitting\. Bases are split 60/20/20 with no cross\-split variants\. Clean\-only and CMI\-augmented selectors share architecture, feature schema, development protocol, and test set; only the addition of in\-domain CMI noisy same\-slot training cases changes\.

## Appendix GHyperparameter Development

Development compared normalization in \{min–max, z\-score\}; selector families were class\-balanced logistic regression \(maximum 3,000 iterations\), the final random forest, and a one\-hidden\-layer MLP \(32 hidden units, maximum 500 iterations, early stopping\)\. Development macro\-F1 selected the classifier\. The fixed\-fusion weight and normalization were selected on development data; test data were not used for tuning\. Backend\-specific Qwen/E5/NV/Linq reruns reuse the frozen random\-forest configuration and perform no new hyperparameter search\.

Encoder robustness keeps native 4096\-dimensional outputs for all models\. E5\-Mistral\-7B\-Instruct and Linq\-Embed\-Mistral use last\-token pooling and their official query instruction; NV\-Embed\-v2 uses its native protocol\. All vectors are L2\-normalized before cosine top\-3 retrieval\. Alternative encoders use maximum length 512 and batch size 16; the primary Qwen runner uses batch size 64 with out\-of\-memory backoff\.

Travel composition and mirrored stability usedeepseek\-v4\-pro, temperature zero, thinking disabled, and at most 512 output tokens\. The five\-source answer experiment uses the same model and decoding controls with a 128\-token limit\. The frozen P0–P3 protocol usesdeepseek\-v4\-flash, temperature zero, API\-default reasoning behavior, and 512 output tokens\.

## Appendix HStatistical Protocol

The paired unit is always the base scenario, not an individual transformed row\. For each comparison, all loads, compositions, access variants, or training regimes derived from onebase\_idare resampled together\. We report paired percentile 95% cluster\-bootstrap intervals\. Table[H\.1](https://arxiv.org/html/2608.07622#A8.T1)records the actual replicate count in the paper\-facing files\.

Table H\.1:Bootstrap configuration by result family\.Initial Travel base selection uses seed 7\. Composition base selection, shuffling, shared\-C0 ordering, and mirrored\-stability sampling use seed 20260721\. The fixed\-L3 diagnostic bootstrap uses seed 20260708\. IAAR splits, classifiers, and the remaining paired analyses use seed 42\. DeepSeek calls use temperature zero, fixed prompts, fixed contexts, and fixed schemas, but the retained API did not expose an explicit sampling seed\. The reviewer artifact therefore includes the realized per\-sample generations and deterministic evaluations; byte\-identical new generation remains subject to provider\-side serving\.

The five\-source answer study evaluates 280 held\-out cases under two training regimes for each of five sources, giving5×280×2=2,8005\\times 280\\times 2=2\{,\}800generated answers\. It has no API or parsing failures\. Reported means are point estimates over held\-out bases; bootstrap intervals are sampling uncertainty and are not described as standard deviations across independent model runs\.

## Appendix IComputing Environment

Experiments ran on Ubuntu 22\.04 \(kernel 6\.5\.0\-41\) with two Intel Xeon Platinum 8352V processors \(144 logical CPUs\), 503 GiB RAM, and eight NVIDIA RTX 4090 GPUs with 24,564 MiB each\. The NVIDIA driver was 555\.58\.02 and PyTorch reported CUDA 12\.8\. Jobs used the Conda environmentreasoningand available GPU devices\.

The captured versions were Python 3\.10\.19, PyTorch 2\.10\.0\+cu128, Transformers 5\.12\.1, scikit\-learn 1\.7\.2, NumPy 2\.2\.5, pandas 2\.2\.3, SciPy 1\.15\.3, Matplotlib 3\.10\.7, OpenAI client 2\.15\.0,rank\-bm250\.2\.2,tiktoken0\.12\.0,accelerate1\.14\.0,faiss\-cpu1\.13\.2, FastAPI 0\.128\.0, and Uvicorn 0\.40\.0\. Model identifiers and provider\-side revisions are recorded as served at experiment time; no weights or API credentials are redistributed\.

## Appendix JAdditional Audited Results

### J\.1Travel Memory Growth

Figure[J\.1](https://arxiv.org/html/2608.07622#A10.F1)reports both access families at every load with base\-cluster intervals\. The trajectories are non\-monotonic: scale changes the operating point, but the severe relationship\-specific failures in the next subsection do not follow from count alone\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x6.png)Figure J\.1:Travel L0–L6 plasticity and mirrored stability\. Error bars are 95% base\-cluster bootstrap intervals\.Table J\.1:Travel growth point estimates\. Parentheses give 95% base\-cluster intervals\.
### J\.2Relationship\-Controlled Composition

Figure[J\.2](https://arxiv.org/html/2608.07622#A10.F2)plots every retained P/S ablation point and its base\-cluster interval\. Table[J\.2](https://arxiv.org/html/2608.07622#A10.T2)gives the same point estimates compactly\. Table[J\.3](https://arxiv.org/html/2608.07622#A10.T3)reports paired C8–C0 and C8 relationship contrasts; intervals are computed on within\-base differences rather than inferred from separate endpoint intervals\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x7.png)Figure J\.2:Plasticity and mirrored stability for every relationship–count ablation\. Count zero is shared within each access family\. Error bars are 95% base\-cluster bootstrap intervals\. Color, marker shape, and line style encode the same relationship, preserving legibility under grayscale printing\.Table J\.2:Plasticity \(P\) and stability \(S\) across memory relationships and counts\. The C0 point is shared within an access family\.Table J\.3:Key paired plasticity contrasts with 95% base\-cluster intervals \(5,000 resamples\)\.
### J\.3Retrieval\-to\-Decision Pathway

Table[J\.4](https://arxiv.org/html/2608.07622#A10.T4)retains the complete C8 exposure partition\. The four exposure cells sum to one in every row\. Conditional denominators are shown because a high conditional success rate based on few exposed targets should not be mistaken for high end\-to\-end success\.

Table J\.4:Count\-8 retrieval\-to\-decision decomposition\.TTis target exposure,CCis competing exposure, andAAis answer success\.
### J\.4Update Authority

![Refer to caption](https://arxiv.org/html/2608.07622v1/x8.png)Figure J\.3:Poison acceptance under fixed\-L3 authority conditions\. P0 is ordinary control/noise, P1 adds recency only, P2 adds update wording, and P3 adds authoritative correction wording\.Table J\.5:Fixed\-L3 poison\-acceptance rates; 100 cases per access and condition\.
### J\.5Cross\-Dataset Growth

Table[J\.6](https://arxiv.org/html/2608.07622#A10.T6)reports L6–L0 answer\-level displacement\. The paired intervals make the architecture contrast explicit: lexical access shows large plasticity losses, while dense retrieval exhibits substantially smaller changes on the same transformed bases\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x9.png)Figure J\.4:Complete external L0–L6 trajectories retained for PERMA and LongMemEval\. Each panel uses the same paired bases across loads\. Per\-load point estimates are shown without pseudo\-replicate error bars; the paired L6–L0 confidence intervals appear in Figure[J\.5](https://arxiv.org/html/2608.07622#A10.F5)\.Table J\.6:Answer\-level L6–L0 displacement with paired 95% base\-cluster intervals\.PnP\_\{n\}denotes valid\-update success with noise\.IAAR is not inserted into this table because the retained selector experiment uses a held\-out noisy\-update cohort rather than the same L0–L6 answer protocol\. Its matched retrieval and answer results are reported separately in Figures[J\.6](https://arxiv.org/html/2608.07622#A10.F6)and[J\.8](https://arxiv.org/html/2608.07622#A10.F8)\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x10.png)Figure J\.5:L6–L0 answer\-success displacement on all four external CMI transformations\. Error bars are paired 95% base\-cluster bootstrap intervals\. Full intermediate\-load outputs were retained for PERMA and LongMemEval; the HorizonBench and MEME archives retain audited endpoints and paired intervals\.Complete endpoint estimates and paired intervals are retained inpaper\_results/chapter5/external\_growth\.csv\. In particular, all four LexicalΔ​P\\Delta Pintervals exclude zero\. These transformations diagnose access under controlled historical growth; they do not replace each source’s native benchmark evaluation\.

### J\.6CMI\-Augmented Selector Retrieval

Table[J\.7](https://arxiv.org/html/2608.07622#A10.T7)reports the held\-out noisy\-update retrieval diagnostic\. CMI augmentation improves target access and MRR on all five sources, while preference ordering does not improve uniformly because target and competing memories can remain co\-exposed\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x11.png)Figure J\.6:Matched clean\-only versus CMI\-augmented IAAR results on five data sources\. The first three panels are retrieval\-level noisy\-update diagnostics; the fourth is answer\-level noisy\-update success\. Both training regimes use the same held\-out cases and selector architecture\.Table J\.7:Clean\-only versus in\-domain CMI\-augmented IAAR retrieval on held\-out noisy updates\. R@3 is target recall, PO is preference ordering, and CE is co\-exposure\.Table J\.8:Paired CMI\-augmentation deltas with 95% base\-cluster intervals\.Figure[J\.7](https://arxiv.org/html/2608.07622#A10.F7)shows that the retrieval gains do not arise from a single constant alpha shift\. CMI augmentation moves noisy\-update cases toward more dense or mixed access on PERMA and HorizonBench, while LongMemEval and MEME retain substantial source\-specific mass at alpha 0\.75\. This distribution is descriptive; alpha itself is not assigned a semantic event label\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x12.png)Figure J\.7:IAAR lexical\-weight distributions on held\-out noisy\-update cases\. Each bar contains 70 cases\. Alpha 0 is pure dense retrieval and alpha 1 is pure lexical retrieval\. No event label is available to the selector at inference\.Table J\.9:Held\-out selector\-label prediction under clean\-only and in\-domain CMI\-augmented training\. These are all\-event test metrics, not answer accuracy\.
### J\.7IAAR Relative to Fixed Retrieval on Matched Cohorts

The aggregate paper\-cutoff record contains a complete same\-cohort comparison among BM25, Dense RAG, RRF, fixed fusion, frozen IAAR, retrained IAAR, and an oracle for PERMA and LongMemEval\. Figure[J\.8](https://arxiv.org/html/2608.07622#A10.F8)reports their noisy\-update retrieval metrics\. We do not extend this figure to HorizonBench, MEME, or Travel because the retained fixed\-retriever summaries for those sources are not on the exact held\-out cohort used by the final selector ablation\. This scope restriction prevents an unmatched comparison\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x13.png)Figure J\.8:Noisy\-update retrieval comparison on matched PERMA and LongMemEval test cohorts \(70 cases per row\)\. “Frozen” transfers the original selector; “Retrain” uses the source\-specific fixed training protocol\. The oracle is an upper bound and is not deployable\.Table[J\.10](https://arxiv.org/html/2608.07622#A10.T10)reports selector\-label prediction separately from end\-to\-end retrieval\. Retraining greatly improves label prediction, but macro F1 remains the relevant audit statistic because the five alpha classes are imbalanced\. These scores do not imply that the selected alpha dominates every fixed retriever on every metric\.

Table J\.10:Frozen and source\-retrained IAAR router metrics on held\-out test variants\.
### J\.8Answer\-Level CMI Augmentation

Table[J\.13](https://arxiv.org/html/2608.07622#A10.T13)reports all matched unnoised and noisy\-update point estimates\. The noisy\-update intervals are complete for all five sources\. The LongMemEval stable\-retention interval and PERMA/LongMemEval absolute wrong\-use rates were not retained; they are left unavailable rather than inferred\.

The Travel paired analysis in Table[J\.13](https://arxiv.org/html/2608.07622#A10.T13)confirms that the aggregate answer improvement is concentrated in noisy updates\. The unnoised interval includes zero and its point estimate is below 0\.005, while the noisy\-update gain is 0\.300\. Incorrect\-memory usage falls by the same paired amount in this specific Travel evaluator; no complementarity assumption is used for sources whose absolute wrong\-use fields were not retained\.

### J\.9External Fixed\-L3 Poisoning Probe

Table[J\.13](https://arxiv.org/html/2608.07622#A10.T13)reports all four external sources under the matched answer\-level fixed\-L3 protocol\. The non\-monotonic source and access differences reinforce that update framing, repetition, and the access mechanism interact rather than inducing a universal severity order\. Together with the TravelPlanner P0–P3 result in Table[J\.5](https://arxiv.org/html/2608.07622#A10.T5), this completes the five\-source fixed\-L3 evidence retained in the appendix\.

Table J\.11:Answer success under clean\-only and in\-domain CMI\-augmented selector training\. Each row has 70 cases\. “CI” is for the paired delta\.Table J\.12:Travel CMI\-augmentation paired answer deltas \(5,000 base\-cluster resamples\)\.
Table J\.13:Poison\-acceptance rates in the external fixed\-L3 answer probe\. Recency adds one invalid recent state; repeated history adds three; latest update gives one invalid memory explicit update status\. Each access\-condition cell contains 184 LongMemEval cases or 200 cases for the other sources\.

### J\.10Native\-4096 Encoder Robustness

Figure[J\.9](https://arxiv.org/html/2608.07622#A10.F9)changes only the dense encoder while keeping native 4096\-dimensional output, cosine top\-3 retrieval, query set, memory set, and composition counts fixed\. It is a robustness check rather than an encoder leaderboard\. Error bars use 1,000 base\-cluster resamples\.

![Refer to caption](https://arxiv.org/html/2608.07622v1/x14.png)Figure J\.9:Target Recall@3 at C2, C4, and C8 for four native\-4096 embedding backends\. Labels are printed inside bars; color remains distinguishable in grayscale through luminance\.Across encoders, matched neutral and unrelated additions are generally less damaging than repeated history or same\-slot conflict, although the magnitude and non\-monotonic details are backend dependent\. The accompanying composition summary and C8–C0 bootstrap CSV preserve the values shown in this figure; fresh per\-sample rankings are reconstructed by the retained encoder script\.

Table J\.14:C8–C0 Target Recall@3 changes under native\-4096 encoder replacement\. Brackets contain 95% base\-cluster bootstrap intervals\.The direction of the relationship effect is robust, but its magnitude is not encoder invariant\. In particular, Linq is comparatively robust to repeated history whereas NV exhibits a larger same\-slot\-conflict loss\. We therefore use the encoder swap as evidence for a recurring relationship ordering, not for a universal ranking among embedding models\.

## Appendix KThreats to Validity and Evidence Boundaries

#### Controlled transformations versus native benchmarks\.

CMI reuses states and queries from external sources but imposes a common historical\-growth protocol\. The reported values diagnose memory interference; they are not replacements for PERMA, LongMemEval, HorizonBench, or MEME native leaderboard scores\. Source\-specific language can still affect lexical and dense retrieval, which is why results are reported by source rather than only as a pooled mean\.

#### Coverage and statistical uncertainty\.

Each source contains 46–50 paired bases\. Cluster bootstrap intervals quantify uncertainty over those bases, not variation across independently trained generators or embedding checkpoints\. HorizonBench and MEME retain audited L0/L6 endpoints and paired intervals but not every intermediate answer\-level summary in the final paper archive; no missing trajectory points are interpolated\. LongMemEval stable\-retention augmentation lacks a retained paired interval, so only its point estimate is reported\.

#### Retrieval and generation scope\.

The primary dense access uses one 4096\-dimensional Qwen encoder, with three same\-dimensional encoder replacements as a retrieval robustness check\. The answer studies use one hosted generator under deterministic decoding controls; provider\-side serving may still change\. Retrieval\-pathway conclusions are therefore limited to the reported lexical, dense, fusion, RRF, and IAAR access mechanisms\.

#### Selector scope\.

IAAR is an exploratory selector trained on oracle\-derived alpha labels, not a general memory\-state reasoner\. Source retraining improves its label prediction and noisy\-update access, but preference ordering remains weak on several sources because target and competitor can be co\-exposed\. The frozen\-versus\- retrained full retriever comparison is available only for matched PERMA and LongMemEval cohorts\. We therefore claim evidence that CMI augmentation can teach useful interference signals, not that IAAR universally dominates fixed retrieval\.

#### Annotation and redistribution\.

Gold target, historical, and forbidden roles are required to construct and audit CMI but are hidden from deployed retrieval, selection, and generation\. LongMemEval old values include an API\-assisted extraction followed by strict evidence\-span verification; 46 of 50 paper\-cutoff candidates pass\. The confidential reviewer artifact includes the exact transformed inputs consumed by the retained experiments, while upstream URLs are retained only for attribution and provenance\. A later public release may use a narrower source\-only boundary where redistribution terms require it\.

## Appendix LArtifact Inventory and Reproduction Procedure

Table L\.1:Command\-level reproduction map\. Exact arguments and defaults are recorded in the named scripts andhyperparameters\.md\.The submitted reviewer artifact contains:

- •code/: the paper\-cutoff construction, retrieval, IAAR, generation, evaluation, bootstrap, and encoder\-robustness implementation;
- •data/: the exact transformed TravelPlanner, PERMA, LongMemEval, HorizonBench, and MEME inputs plus construction and protocol audits;
- •results/paper\_tables/: aggregate paper\-facing CSVs and paired bootstrap intervals;
- •results/answer\_generation/,results/fixed\_l3\_poisoning/, andresults/encoder\_robustness/: realized per\-sample outputs retained for offline scoring and figure verification;
- •docs/: environment, dependencies, hyperparameters, seeds, licenses, and third\-party notices; and
- •README\.md,DATA\_MANIFEST\.md,validate\_artifact\.py, andFILE\_MANIFEST\.sha256\.

No external paper code or data repository is required to inspect the submitted experiments\. Model checkpoints and API services are not bundled because of size and provider terms; exact identifiers and settings are recorded indocs/hyperparameters\.md\. The bundled outputs permit answer scoring and statistical checks without new API calls\.

The recommended reproduction order is: unpack the archive; verifyFILE\_MANIFEST\.sha256; runvalidate\_artifact\.py; create theCMIenvironment; execute retrieval on the includeddata/; use the bundled outputs for deterministic scoring and base\-cluster summaries; and only then repeat model\-backed retrieval or generation if desired\. Hugging Face and DeepSeek credentials are needed only for fresh model calls, not for the offline audit\.

The original experiments ran in the server environmentreasoning; clean reproduction was validated in a newly created environment namedCMI\. The captured repository HEAD was6cd9de14b71915e39ac742a20dc33785e14b6aab, but the working tree contained experiment changes\. The submitted file manifest, rather than the Git hash alone, identifies the code, data, and result snapshot\.

No API keys, model weights, or embedding caches are included\. The archive does include the transformed paper inputs and realized per\-sample outputs needed for review\. Known provenance limitations are retained explicitly: the standalone PERMA and LongMemEval answer summaries were recovered from an audited consolidated evidence record; unsupported absolute wrong\-use values and the unavailable LongMemEval stable\-retention interval are not reconstructed\.